数据转换

目录

• 1 前提准备
  • 1.1 载入报
  • 1.2 flights 数据集
  • 1.3 数据类型介绍
  • 1.4 dplyr 基础
• 2 用 filter() 筛选行
  • 2.1 按给定的逻辑判断筛选出符合要求的子数据集
  • 2.2 对某个变量选取两个或以上的值
  • 2.3 缺失值
  • 2.4 between()
• 3 用 arrange() 排列行
• 4 用 select() 筛选列
  • 4.1 基本用法
  • 4.2 Helper Functions
  • 4.3 利用 everything() 将列移到最前面
  • 4.4 rename() 函数
• 5 用 mutate() 添加列
  • 5.1 基本用法
  • 5.2 可以用于计算新变量的向量化运算符
• 6 用 summarise() 计算分组统计量
  • 6.1 group_by() 和 summarise() 联合使用，计算分组统计量
  • 6.2 管道操作符 %>%
  • 6.3 缺失值
  • 6.4 计数（count）
  • 6.5 聚合统计函数
  • 6.6 依据多个变量进行分组
  • 6.7 取消分组
• 7 分组变换（mutate(), filter()）

1 前提准备

1.1 载入包

library(nycflights13)

程辑包‘nycflights13’是用R版本3.3.2 来建造的

library(tidyverse)

程辑包‘tidyverse’是用R版本3.3.2 来建造的Loading tidyverse: ggplot2
Loading tidyverse: tibble
Loading tidyverse: tidyr
Loading tidyverse: readr
Loading tidyverse: purrr
Loading tidyverse: dplyr
程辑包‘ggplot2’是用R版本3.3.2 来建造的程辑包‘tidyr’是用R版本3.3.2 来建造的程辑包‘readr’是用R版本3.3.2 来建造的程辑包‘purrr’是用R版本3.3.2 来建造的程辑包‘dplyr’是用R版本3.3.2 来建造的Conflicts with tidy packages -------------------------------------------------------------------------------------
filter(): dplyr, stats
lag():    dplyr, stats

1.2 flights 数据集

• flights 数据集以 tibble 形式存储

flights

#> # A tibble: 336,776 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1  2013     1     1      517            515         2      830
#> 2  2013     1     1      533            529         4      850
#> 3  2013     1     1      542            540         2      923
#> 4  2013     1     1      544            545        -1     1004
#> 5  2013     1     1      554            600        -6      812
#> 6  2013     1     1      554            558        -4      740
#> # ... with 3.368e+05 more rows, and 12 more variables:
#> #   sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, flight <int>,
#> #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> #   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

• 使用 View 可以观察完整的 flights 数据集

  View(flights)

1.3 数据类型介绍

• int stands for integers.

• dbl stands for doubles, or real numbers.

• chr stands for character vectors, or strings.

• dttm stands for date-times (a date + a time).

• lgl stands for logical, vectors that contain only TRUE or FALSE.

• fctr stands for factors, which R uses to represent categorical variables with fixed possible values.

• date stands for dates.

1.4 dplyr 基础

接下来，将会接触到5种核心的 dplyr 函数

• Pick observations by their values filter().

• Reorder the rows arrange().

• Pick variables by their names select().

• Create new variables with functions of existing variables mutate().

• Collapse many values down to a single summary summarise().

2 用 filter() 筛选行

2.1 按给定的逻辑判断筛选出符合要求的子数据集, 类似于 base::subset() 函数

filter(flights, month == 1, day == 1)

#> # A tibble: 842 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1  2013     1     1      517            515         2      830
#> 2  2013     1     1      533            529         4      850
#> 3  2013     1     1      542            540         2      923
#> 4  2013     1     1      544            545        -1     1004
#> 5  2013     1     1      554            600        -6      812
#> 6  2013     1     1      554            558        -4      740
#> # ... with 836 more rows, and 12 more variables: sched_arr_time <int>,
#> #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>

(dec25 <- filter(flights, month == 12, day == 25))

#> # A tibble: 719 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1  2013    12    25      456            500        -4      649
#> 2  2013    12    25      524            515         9      805
#> 3  2013    12    25      542            540         2      832
#> 4  2013    12    25      546            550        -4     1022
#> 5  2013    12    25      556            600        -4      730
#> 6  2013    12    25      557            600        -3      743
#> # ... with 713 more rows, and 12 more variables: sched_arr_time <int>,
#> #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>

2.2 对某个变量选取两个或以上的值

filter(flights, month == 11 | month == 12)

filter(flights, month %in% c(11, 12))

2.3 缺失值

• NA (“not availables”) 代表缺失值，在计算中有“传染性”：对 NA 进行任意操作，都会返回 NA。

NA > 5

[1] NA

10 == NA

[1] NA

NA + 10

[1] NA

NA / 2

[1] NA

NA == NA

[1] NA

x <- NA
y <- NA
x == y

[1] NA

• 用 is.na() 判断是否为缺失值

is.na(x)

[1] TRUE

df <- tibble(x = c(1, NA, 3))
filter(df, x > 1)

#> # A tibble: 1 × 1
#>       x
#>   <dbl>
#> 1     3

• filter() 会排除 False 和 NA 的行，仅仅返回结果为 True 的行。如果想要保留缺失值，需要进行显式声明

filter(df, is.na(x) | x > 1)

#> # A tibble: 2 × 1
#>       x
#>   <dbl>
#> 1    NA
#> 2     3

2.4 between()

df <- tibble(x = c(1, 2, 2, 3, 4))
filter(df, between(x, 1, 3))

#> # A tibble: 4 × 1
#>       x
#>   <dbl>
#> 1     1
#> 2     2
#> 3     2
#> 4     3

3 用 arrange() 排列行

• 按给定的列名依次对行进行排序

arrange(flights, year, month, day)

#> # A tibble: 336,776 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1  2013     1     1      517            515         2      830
#> 2  2013     1     1      533            529         4      850
#> 3  2013     1     1      542            540         2      923
#> 4  2013     1     1      544            545        -1     1004
#> 5  2013     1     1      554            600        -6      812
#> 6  2013     1     1      554            558        -4      740
#> # ... with 3.368e+05 more rows, and 12 more variables:
#> #   sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, flight <int>,
#> #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> #   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

• 用 desc() 进行降序排列

arrange(flights, desc(arr_delay))

#> # A tibble: 336,776 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1  2013     1     9      641            900      1301     1242
#> 2  2013     6    15     1432           1935      1137     1607
#> 3  2013     1    10     1121           1635      1126     1239
#> 4  2013     9    20     1139           1845      1014     1457
#> 5  2013     7    22      845           1600      1005     1044
#> 6  2013     4    10     1100           1900       960     1342
#> # ... with 3.368e+05 more rows, and 12 more variables:
#> #   sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, flight <int>,
#> #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> #   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

• 缺失值将会被放置在数据集的末尾

df <- tibble(x = c(5, 2, NA))
arrange(df, x)

#> # A tibble: 3 × 1
#>       x
#>   <dbl>
#> 1     2
#> 2     5
#> 3    NA

arrange(df, desc(x))

#> # A tibble: 3 × 1
#>       x
#>   <dbl>
#> 1     5
#> 2     2
#> 3    NA

4 用 select() 筛选列

4.1 基本用法

• 选择year、month、day列

select(flights, year, month, day)

#> # A tibble: 336,776 × 3
#>    year month   day
#>   <int> <int> <int>
#> 1  2013     1     1
#> 2  2013     1     1
#> 3  2013     1     1
#> 4  2013     1     1
#> 5  2013     1     1
#> 6  2013     1     1
#> # ... with 3.368e+05 more rows

• 选择year和day之间的列（包括）

select(flights, year:day)

#> # A tibble: 336,776 × 3
#>    year month   day
#>   <int> <int> <int>
#> 1  2013     1     1
#> 2  2013     1     1
#> 3  2013     1     1
#> 4  2013     1     1
#> 5  2013     1     1
#> 6  2013     1     1
#> # ... with 3.368e+05 more rows

• 选择除year和day之间的列以外的所有列（包括）

select(flights, -(year:day))

#> # A tibble: 336,776 × 16
#>   dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay
#>      <int>          <int>     <dbl>    <int>          <int>     <dbl>
#> 1      517            515         2      830            819        11
#> 2      533            529         4      850            830        20
#> 3      542            540         2      923            850        33
#> 4      544            545        -1     1004           1022       -18
#> 5      554            600        -6      812            837       -25
#> 6      554            558        -4      740            728        12
#> # ... with 3.368e+05 more rows, and 10 more variables: carrier <chr>,
#> #   flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> #   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

4.2 Helper Functions

• starts_with(x, ignore.case = TRUE): names starts with x.

• ends_with(x, ignore.case = TRUE): names ends in x.

• contains(x, ignore.case = TRUE): selects all variables whose name contains x.

• matches(x, ignore.case = TRUE): selects all variables whose name matches the regular expression x.

• num_range("x", 1:5, width = 2): selects all variables (numerically) from x01 to x05.

• one_of("x", "y", "z"): selects variables provided in a character vector.

• everything(): selects all variables.

4.3 利用 everything() 将列移到最前面

select(flights, time_hour, air_time, everything())

#> # A tibble: 336,776 × 19
#>             time_hour air_time  year month   day dep_time sched_dep_time
#>                <dttm>    <dbl> <int> <int> <int>    <int>          <int>
#> 1 2013-01-01 05:00:00      227  2013     1     1      517            515
#> 2 2013-01-01 05:00:00      227  2013     1     1      533            529
#> 3 2013-01-01 05:00:00      160  2013     1     1      542            540
#> 4 2013-01-01 05:00:00      183  2013     1     1      544            545
#> 5 2013-01-01 06:00:00      116  2013     1     1      554            600
#> 6 2013-01-01 05:00:00      150  2013     1     1      554            558
#> # ... with 3.368e+05 more rows, and 12 more variables: dep_delay <dbl>,
#> #   arr_time <int>, sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,
#> #   flight <int>, tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>,
#> #   hour <dbl>, minute <dbl>

4.4 rename() 函数

rename(flights, tail_num = tailnum)

#> # A tibble: 336,776 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1  2013     1     1      517            515         2      830
#> 2  2013     1     1      533            529         4      850
#> 3  2013     1     1      542            540         2      923
#> 4  2013     1     1      544            545        -1     1004
#> 5  2013     1     1      554            600        -6      812
#> 6  2013     1     1      554            558        -4      740
#> # ... with 3.368e+05 more rows, and 12 more variables:
#> #   sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, flight <int>,
#> #   tail_num <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> #   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

5 用 mutate() 添加列

5.1 基本用法

• 对已有列进行数据运算并添加为新列

flights_sml <- select(flights, 
  year:day, 
  ends_with("delay"), 
  distance, 
  air_time
)
mutate(flights_sml,
  gain = arr_delay - dep_delay,
  speed = distance / air_time * 60
)

#> # A tibble: 336,776 × 9
#>    year month   day dep_delay arr_delay distance air_time  gain speed
#>   <int> <int> <int>     <dbl>     <dbl>    <dbl>    <dbl> <dbl> <dbl>
#> 1  2013     1     1         2        11     1400      227     9   370
#> 2  2013     1     1         4        20     1416      227    16   374
#> 3  2013     1     1         2        33     1089      160    31   408
#> 4  2013     1     1        -1       -18     1576      183   -17   517
#> 5  2013     1     1        -6       -25      762      116   -19   394
#> 6  2013     1     1        -4        12      719      150    16   288
#> # ... with 3.368e+05 more rows

• 可以在同一语句中对刚增加的列进行操作

mutate(flights_sml,
  gain = arr_delay - dep_delay,
  hours = air_time / 60,
  gain_per_hour = gain / hours
)

#> # A tibble: 336,776 × 10
#>    year month   day dep_delay arr_delay distance air_time  gain hours
#>   <int> <int> <int>     <dbl>     <dbl>    <dbl>    <dbl> <dbl> <dbl>
#> 1  2013     1     1         2        11     1400      227     9  3.78
#> 2  2013     1     1         4        20     1416      227    16  3.78
#> 3  2013     1     1         2        33     1089      160    31  2.67
#> 4  2013     1     1        -1       -18     1576      183   -17  3.05
#> 5  2013     1     1        -6       -25      762      116   -19  1.93
#> 6  2013     1     1        -4        12      719      150    16  2.50
#> # ... with 3.368e+05 more rows, and 1 more variables: gain_per_hour <dbl>

• 用 transmute() 仅仅保留新增的列

transmute(flights,
  gain = arr_delay - dep_delay,
  hours = air_time / 60,
  gain_per_hour = gain / hours
)

#> # A tibble: 336,776 × 3
#>    gain hours gain_per_hour
#>   <dbl> <dbl>         <dbl>
#> 1     9  3.78          2.38
#> 2    16  3.78          4.23
#> 3    31  2.67         11.62
#> 4   -17  3.05         -5.57
#> 5   -19  1.93         -9.83
#> 6    16  2.50          6.40
#> # ... with 3.368e+05 more rows

5.2 可以用于计算新变量的向量化运算符

• Arithmetic operators: +, -, *, /, ^.

• Modular arithmetic: %/% (integer division) and %% (remainder).

transmute(flights,
  dep_time,
  hour = dep_time %/% 100,
  minute = dep_time %% 100
)

#> # A tibble: 336,776 × 3
#>   dep_time  hour minute
#>      <int> <dbl>  <dbl>
#> 1      517     5     17
#> 2      533     5     33
#> 3      542     5     42
#> 4      544     5     44
#> 5      554     5     54
#> 6      554     5     54
#> # ... with 3.368e+05 more rows

• Logs: log(), log2(), log10().

• Offsets: lead() and lag() allow you to refer to leading or lagging values.

(x <- 1:10)

 [1]  1  2  3  4  5  6  7  8  9 10

lag(x)

 [1] NA  1  2  3  4  5  6  7  8  9

lead(x)

 [1]  2  3  4  5  6  7  8  9 10 NA

# compute running differences
x - lag(x)

 [1] NA  1  1  1  1  1  1  1  1  1

# find when values change
x != lag(x)

 [1]   NA TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

• Cumulative and rolling aggregates: cumsum(), cumprod(), cummin(), cummax(), cummean().
  If you need rolling aggregates (i.e. a sum computed over a rolling window), try the RcppRoll package.

x

 [1]  1  2  3  4  5  6  7  8  9 10

cumsum(x)

 [1]  1  3  6 10 15 21 28 36 45 55

cummean(x)

 [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

• Logical comparisons, <, <=, >, >=, !=.

• Ranking: min_rank(), row_number(), dense_rank(), percent_rank(), cume_dist(), ntile().

(y <- c(1, 2, 2, NA, 3, 4))

[1]  1  2  2 NA  3  4

min_rank(y)

[1]  1  2  2 NA  4  5

min_rank(desc(y))

[1]  5  3  3 NA  2  1

row_number(y)

[1]  1  2  3 NA  4  5

dense_rank(y)

[1]  1  2  2 NA  3  4

percent_rank(y)

[1] 0.00 0.25 0.25   NA 0.75 1.00

cume_dist(y)

[1] 0.2 0.6 0.6  NA 0.8 1.0

6 用 summarise() 计算分组统计量

6.1 group_by() 和 summarise() 联合使用，计算分组统计量

summarise(flights, delay = mean(dep_delay, na.rm = TRUE))

#> # A tibble: 1 × 1
#>   delay
#>   <dbl>
#> 1  12.6

by_day <- group_by(flights, year, month, day)
summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))

#> Source: local data frame [365 x 4]
#> Groups: year, month [?]
#> 
#>    year month   day delay
#>   <int> <int> <int> <dbl>
#> 1  2013     1     1 11.55
#> 2  2013     1     2 13.86
#> 3  2013     1     3 10.99
#> 4  2013     1     4  8.95
#> 5  2013     1     5  5.73
#> 6  2013     1     6  7.15
#> # ... with 359 more rows

6.2 管道操作符 %>%

• 探索对于不同地点，平均飞行距离与平均延误时间之间的关系。可以发现，平均飞行距离为750英里时，平均延误时间达到峰值，可能是由于更长的飞行距离，为弥补延误提供了可能。

by_dest <- group_by(flights, dest)
delay <- summarise(by_dest,
  count = n(),
  dist = mean(distance, na.rm = TRUE),
  delay = mean(arr_delay, na.rm = TRUE)
)
(delay <- filter(delay, count > 20, dest != "HNL"))

#> # A tibble: 96 × 4
#>     dest count      dist     delay
#>    <chr> <int>     <dbl>     <dbl>
#> 1    ABQ   254 1826.0000  4.381890
#> 2    ACK   265  199.0000  4.852273
#> 3    ALB   439  143.0000 14.397129
#> 4    ATL 17215  757.1082 11.300113
#> 5    AUS  2439 1514.2530  6.019909
#> 6    AVL   275  583.5818  8.003831
#> 7    BDL   443  116.0000  7.048544
#> 8    BGR   375  378.0000  8.027933
#> 9    BHM   297  865.9966 16.877323
#> 10   BNA  6333  758.2135 11.812459
#> # ... with 86 more rows

ggplot(data = delay, mapping = aes(x = dist, y = delay)) +
  geom_point(aes(size = count), alpha = 1/3) +
  geom_smooth(se = FALSE)

• 数据预处理涉及3个步骤：
  • 依据目的地分组
  • 计算分组后的平均飞行距离、平均延误时间和航班数
  • 过滤掉夏威夷机场（平均飞行距离是其他机场的2倍以上）和航班数过少的目的地
• 使用管道操作符 %>% 完成以上3个步骤

delays <- flights %>% 
  group_by(dest) %>% 
  summarise(
    count = n(),
    dist = mean(distance, na.rm = TRUE),
    delay = mean(arr_delay, na.rm = TRUE)
  ) %>% 
  filter(count > 20, dest != "HNL")

6.3 缺失值

• 缺失值传入聚合函数，也会返回缺失值

flights %>% 
  group_by(year, month, day) %>% 
  summarise(mean = mean(dep_delay))

#> Source: local data frame [365 x 4]
#> Groups: year, month [?]
#> 
#>    year month   day  mean
#>   <int> <int> <int> <dbl>
#> 1  2013     1     1    NA
#> 2  2013     1     2    NA
#> 3  2013     1     3    NA
#> 4  2013     1     4    NA
#> 5  2013     1     5    NA
#> 6  2013     1     6    NA
#> # ... with 359 more rows

• 使用 na.rm 参数

flights %>% 
  group_by(year, month, day) %>% 
  summarise(mean = mean(dep_delay, na.rm = TRUE))

#> Source: local data frame [365 x 4]
#> Groups: year, month [?]
#> 
#>    year month   day  mean
#>   <int> <int> <int> <dbl>
#> 1  2013     1     1 11.55
#> 2  2013     1     2 13.86
#> 3  2013     1     3 10.99
#> 4  2013     1     4  8.95
#> 5  2013     1     5  5.73
#> 6  2013     1     6  7.15
#> # ... with 359 more rows

• 先行剔除 NA 值

not_cancelled <- flights %>% 
  filter(!is.na(dep_delay), !is.na(arr_delay))
not_cancelled %>% 
  group_by(year, month, day) %>% 
  summarise(mean = mean(dep_delay))

#> Source: local data frame [365 x 4]
#> Groups: year, month [?]
#> 
#>    year month   day  mean
#>   <int> <int> <int> <dbl>
#> 1  2013     1     1 11.44
#> 2  2013     1     2 13.68
#> 3  2013     1     3 10.91
#> 4  2013     1     4  8.97
#> 5  2013     1     5  5.73
#> 6  2013     1     6  7.15
#> # ... with 359 more rows

6.4 计数（count）

在计算分组统计量的时候，需要考虑组内样本的个数（n()）、非缺失值的个数（sum(!is.na(x))）以及分组的个数

• 用 tailnum 区分飞机，用核密度图分析飞机的平均延误时间分布情况

delays <- not_cancelled %>% 
  group_by(tailnum) %>% 
  summarise(
    delay = mean(arr_delay)
  )
delays

#> # A tibble: 4,037 × 2
#>    tailnum      delay
#>      <chr>      <dbl>
#> 1   D942DN 31.5000000
#> 2   N0EGMQ  9.9829545
#> 3   N10156 12.7172414
#> 4   N102UW  2.9375000
#> 5   N103US -6.9347826
#> 6   N104UW  1.8043478
#> 7   N10575 20.6914498
#> 8   N105UW -0.2666667
#> 9   N107US -5.7317073
#> 10  N108UW -1.2500000
#> # ... with 4,027 more rows

ggplot(data = delays, mapping = aes(x = delay)) + 
  geom_freqpoly(binwidth = 10)

• 用 n() 计算组内样本个数，从散点图可以看出，飞行次数少的飞机，平均延误时间的变异程度更大

delays <- not_cancelled %>% 
  group_by(tailnum) %>% 
  summarise(
    delay = mean(arr_delay, na.rm = TRUE),
    n = n()
  )
delays

#> # A tibble: 4,037 × 3
#>    tailnum      delay     n
#>      <chr>      <dbl> <int>
#> 1   D942DN 31.5000000     4
#> 2   N0EGMQ  9.9829545   352
#> 3   N10156 12.7172414   145
#> 4   N102UW  2.9375000    48
#> 5   N103US -6.9347826    46
#> 6   N104UW  1.8043478    46
#> 7   N10575 20.6914498   269
#> 8   N105UW -0.2666667    45
#> 9   N107US -5.7317073    41
#> 10  N108UW -1.2500000    60
#> # ... with 4,027 more rows

ggplot(data = delays, mapping = aes(x = n, y = delay)) + 
  geom_point(alpha = 1/10)

• 利用 filter() 筛选出飞行次数大于25的飞机，分析它们的延误情况

• ba 代表击打成功率， ab 代表击打次数，击打成功率与击打数正相关，可能是因为球队倾向于让成功率高的球员拥有更多出场机会

# Convert to a tibble so it prints nicely
batting <- as_tibble(Lahman::Batting)
batters <- batting %>% 
  group_by(playerID) %>% 
  summarise(
    ba = sum(H, na.rm = TRUE) / sum(AB, na.rm = TRUE),
    ab = sum(AB, na.rm = TRUE)
  )
batters %>% 
  filter(ab > 100) %>% 
  ggplot(mapping = aes(x = ab, y = ba)) +
    geom_point() + 
    geom_smooth(se = FALSE)

• 依据 ba 进行降序排列，可以看出，击打成功率最高的球员，击打次数只有一次，纯粹是由于运气而非技术

batters %>% 
  arrange(desc(ba))

#> # A tibble: 18,659 × 3
#>    playerID    ba    ab
#>       <chr> <dbl> <int>
#> 1 abramge01     1     1
#> 2 banisje01     1     1
#> 3 bartocl01     1     1
#> 4  bassdo01     1     1
#> 5 birasst01     1     2
#> 6 bruneju01     1     1
#> # ... with 1.865e+04 more rows

6.5 聚合统计函数

• Measures of location: mean(x), median(x).

not_cancelled %>% 
  group_by(year, month, day) %>% 
  summarise(
    avg_delay1 = mean(arr_delay),
    avg_delay2 = mean(arr_delay[arr_delay > 0]) # the average positive delay
  )

#> Source: local data frame [365 x 5]
#> Groups: year, month [?]
#> 
#>    year month   day avg_delay1 avg_delay2
#>   <int> <int> <int>      <dbl>      <dbl>
#> 1  2013     1     1      12.65       32.5
#> 2  2013     1     2      12.69       32.0
#> 3  2013     1     3       5.73       27.7
#> 4  2013     1     4      -1.93       28.3
#> 5  2013     1     5      -1.53       22.6
#> 6  2013     1     6       4.24       24.4
#> # ... with 359 more rows

• Measures of spread: sd(x), IQR(x), mad(x).

# Why is distance to some destinations more variable than to others?
not_cancelled %>% 
  group_by(dest) %>% 
  summarise(distance_sd = sd(distance)) %>% 
  arrange(desc(distance_sd))

#> # A tibble: 104 × 2
#>    dest distance_sd
#>   <chr>       <dbl>
#> 1   EGE       10.54
#> 2   SAN       10.35
#> 3   SFO       10.22
#> 4   HNL       10.00
#> 5   SEA        9.98
#> 6   LAS        9.91
#> # ... with 98 more rows

• Measures of rank: min(x), quantile(x, 0.25), max(x).

# When do the first and last flights leave each day?
not_cancelled %>% 
  group_by(year, month, day) %>% 
  summarise(
    first = min(dep_time),
    last = max(dep_time)
  )

#> Source: local data frame [365 x 5]
#> Groups: year, month [?]
#> 
#>    year month   day first  last
#>   <int> <int> <int> <int> <int>
#> 1  2013     1     1   517  2356
#> 2  2013     1     2    42  2354
#> 3  2013     1     3    32  2349
#> 4  2013     1     4    25  2358
#> 5  2013     1     5    14  2357
#> 6  2013     1     6    16  2355
#> # ... with 359 more rows

• Measures of position: first(x), nth(x, 2), last(x).

not_cancelled %>% 
  group_by(year, month, day) %>% 
  summarise(
    first_dep = first(dep_time), 
    last_dep = last(dep_time)
  )

#> Source: local data frame [365 x 5]
#> Groups: year, month [?]
#> 
#>    year month   day first_dep last_dep
#>   <int> <int> <int>     <int>    <int>
#> 1  2013     1     1       517     2356
#> 2  2013     1     2        42     2354
#> 3  2013     1     3        32     2349
#> 4  2013     1     4        25     2358
#> 5  2013     1     5        14     2357
#> 6  2013     1     6        16     2355
#> # ... with 359 more rows

同样的效果，可以通过以下方法实现，并且会保留所有的变量，注意 mutate(), min_rank, filter() 在这里的作用

not_cancelled %>% 
  group_by(year, month, day) %>% 
  mutate(r = min_rank(desc(dep_time))) %>% 
  filter(r %in% range(r))

#> Source: local data frame [770 x 20]
#> Groups: year, month, day [365]
#> 
#>    year month   day dep_time sched_dep_time dep_delay arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1  2013     1     1      517            515         2      830
#> 2  2013     1     1     2356           2359        -3      425
#> 3  2013     1     2       42           2359        43      518
#> 4  2013     1     2     2354           2359        -5      413
#> 5  2013     1     3       32           2359        33      504
#> 6  2013     1     3     2349           2359       -10      434
#> # ... with 764 more rows, and 13 more variables: sched_arr_time <int>,
#> #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>, r <int>

• Counts: count(), n(), sum(!is.na(x)), n_distinct(x).

# Which destinations have the most carriers?
not_cancelled %>% 
  group_by(dest) %>% 
  summarise(carriers = n_distinct(carrier)) %>% 
  arrange(desc(carriers))

#> # A tibble: 104 × 2
#>    dest carriers
#>   <chr>    <int>
#> 1   ATL        7
#> 2   BOS        7
#> 3   CLT        7
#> 4   ORD        7
#> 5   TPA        7
#> 6   AUS        6
#> # ... with 98 more rows

强大的 count() 函数，此处计算每个机场作为目的地的航班数

not_cancelled %>% 
  count(dest)

#> # A tibble: 104 × 2
#>    dest     n
#>   <chr> <int>
#> 1   ABQ   254
#> 2   ACK   264
#> 3   ALB   418
#> 4   ANC     8
#> 5   ATL 16837
#> 6   AUS  2411
#> # ... with 98 more rows

count() 可以用于加权计数，此处计算每架飞机的总飞行里程

not_cancelled %>% 
  count(tailnum, wt = distance)

#> # A tibble: 4,037 × 2
#>   tailnum      n
#>     <chr>  <dbl>
#> 1  D942DN   3418
#> 2  N0EGMQ 239143
#> 3  N10156 109664
#> 4  N102UW  25722
#> 5  N103US  24619
#> 6  N104UW  24616
#> # ... with 4,031 more rows

• Counts and proportions of logical values: sum(x > 10), mean(y == 0). sum(x) gives the number of TRUEs in x, and mean(x) gives the proportion.

# How many flights left before 5am? (these usually indicate delayed
# flights from the previous day)
not_cancelled %>% 
  group_by(year, month, day) %>% 
  summarise(n_early = sum(dep_time < 500))

#> Source: local data frame [365 x 4]
#> Groups: year, month [?]
#> 
#>    year month   day n_early
#>   <int> <int> <int>   <int>
#> 1  2013     1     1       0
#> 2  2013     1     2       3
#> 3  2013     1     3       4
#> 4  2013     1     4       3
#> 5  2013     1     5       3
#> 6  2013     1     6       2
#> # ... with 359 more rows

# What proportion of flights are delayed by more than an hour?
not_cancelled %>% 
  group_by(year, month, day) %>% 
  summarise(hour_perc = mean(arr_delay > 60))

#> Source: local data frame [365 x 4]
#> Groups: year, month [?]
#> 
#>    year month   day hour_perc
#>   <int> <int> <int>     <dbl>
#> 1  2013     1     1    0.0722
#> 2  2013     1     2    0.0851
#> 3  2013     1     3    0.0567
#> 4  2013     1     4    0.0396
#> 5  2013     1     5    0.0349
#> 6  2013     1     6    0.0470
#> # ... with 359 more rows

6.6 依据多个变量进行分组

创建依据多个变量的分组后，每一次 summarise都会剥离一层分组变量

• 依据year, month, day 三个变量分组

daily <- group_by(flights, year, month, day)
(per_day   <- summarise(daily, flights = n()))

#> Source: local data frame [365 x 4]
#> Groups: year, month [?]
#> 
#>    year month   day flights
#>   <int> <int> <int>   <int>
#> 1  2013     1     1     842
#> 2  2013     1     2     943
#> 3  2013     1     3     914
#> 4  2013     1     4     915
#> 5  2013     1     5     720
#> 6  2013     1     6     832
#> # ... with 359 more rows

• summarise 后，剥离 day 变量

(per_month <- summarise(per_day, flights = sum(flights)))

#> Source: local data frame [12 x 3]
#> Groups: year [?]
#> 
#>    year month flights
#>   <int> <int>   <int>
#> 1  2013     1   27004
#> 2  2013     2   24951
#> 3  2013     3   28834
#> 4  2013     4   28330
#> 5  2013     5   28796
#> 6  2013     6   28243
#> # ... with 6 more rows

• summarise 后，剥离 month 变量

(per_year  <- summarise(per_month, flights = sum(flights)))

#> # A tibble: 1 × 2
#>    year flights
#>   <int>   <int>
#> 1  2013  336776

6.7 取消分组

daily %>% 
  ungroup() %>%             # no longer grouped by date
  summarise(flights = n())  # all flights

#> # A tibble: 1 × 1
#>   flights
#>     <int>
#> 1  336776

7 分组变换（mutate(), filter()）

• 找出每个分组中情况最差的成员

flights_sml %>% 
  group_by(year, month, day) %>%
  filter(rank(desc(arr_delay)) < 10)

#> Source: local data frame [3,306 x 7]
#> Groups: year, month, day [365]
#> 
#>    year month   day dep_delay arr_delay distance air_time
#>   <int> <int> <int>     <dbl>     <dbl>    <dbl>    <dbl>
#> 1  2013     1     1       853       851      184       41
#> 2  2013     1     1       290       338     1134      213
#> 3  2013     1     1       260       263      266       46
#> 4  2013     1     1       157       174      213       60
#> 5  2013     1     1       216       222      708      121
#> 6  2013     1     1       255       250      589      115
#> # ... with 3,300 more rows

• 找出满足一定条件的组别

popular_dests <- flights %>% 
  group_by(dest) %>% 
  filter(n() > 365)
popular_dests

#> Source: local data frame [332,577 x 19]
#> Groups: dest [77]
#> 
#>    year month   day dep_time sched_dep_time dep_delay arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1  2013     1     1      517            515         2      830
#> 2  2013     1     1      533            529         4      850
#> 3  2013     1     1      542            540         2      923
#> 4  2013     1     1      544            545        -1     1004
#> 5  2013     1     1      554            600        -6      812
#> 6  2013     1     1      554            558        -4      740
#> # ... with 3.326e+05 more rows, and 12 more variables:
#> #   sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, flight <int>,
#> #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> #   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

• 分组计算统计量

popular_dests %>% 
  filter(arr_delay > 0) %>% 
  mutate(prop_delay = arr_delay / sum(arr_delay)) %>% 
  select(year:day, dest, arr_delay, prop_delay)

#> Source: local data frame [131,106 x 6]
#> Groups: dest [77]
#> 
#>    year month   day  dest arr_delay prop_delay
#>   <int> <int> <int> <chr>     <dbl>      <dbl>
#> 1  2013     1     1   IAH        11   1.11e-04
#> 2  2013     1     1   IAH        20   2.01e-04
#> 3  2013     1     1   MIA        33   2.35e-04
#> 4  2013     1     1   ORD        12   4.24e-05
#> 5  2013     1     1   FLL        19   9.38e-05
#> 6  2013     1     1   ORD         8   2.83e-05
#> # ... with 1.311e+05 more rows